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Floral signals evolve in a predictable way under artificial and pollinator selection in Brassica rapa
BACKGROUND: Angiosperms employ an astonishing variety of visual and olfactory floral signals that are generally thought to evolve under natural selection. Those morphological and chemical traits can form highly correlated sets of traits. It is not always clear which of these are used by pollinators...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517814/ https://www.ncbi.nlm.nih.gov/pubmed/32972368 http://dx.doi.org/10.1186/s12862-020-01692-7 |
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author | Zu, Pengjuan Schiestl, Florian P. Gervasi, Daniel Li, Xin Runcie, Daniel Guillaume, Frédéric |
author_facet | Zu, Pengjuan Schiestl, Florian P. Gervasi, Daniel Li, Xin Runcie, Daniel Guillaume, Frédéric |
author_sort | Zu, Pengjuan |
collection | PubMed |
description | BACKGROUND: Angiosperms employ an astonishing variety of visual and olfactory floral signals that are generally thought to evolve under natural selection. Those morphological and chemical traits can form highly correlated sets of traits. It is not always clear which of these are used by pollinators as primary targets of selection and which would be indirectly selected by being linked to those primary targets. Quantitative genetics tools for predicting multiple traits response to selection have been developed since long and have advanced our understanding of evolution of genetically correlated traits in various biological systems. We use these tools to predict the evolutionary trajectories of floral traits and understand the selection pressures acting on them. RESULTS: We used data from an artificial selection and a pollinator (bumblebee, hoverfly) evolution experiment with fast cycling Brassica rapa plants to predict evolutionary changes of 12 floral volatiles and 4 morphological floral traits in response to selection. Using the observed selection gradients and the genetic variance-covariance matrix (G-matrix) of the traits, we showed that the observed responses of most floral traits including volatiles were predicted in the right direction in both artificial- and bumblebee-selection experiment. Genetic covariance had a mix of constraining and facilitating effects on evolutionary responses. We further revealed that G-matrices also evolved in the selection processes. CONCLUSIONS: Overall, our integrative study shows that floral signals, especially volatiles, evolve under selection in a mostly predictable way, at least during short term evolution. Evolutionary constraints stemming from genetic covariance affected traits evolutionary trajectories and thus it is important to include genetic covariance for predicting the evolutionary changes of a comprehensive suite of traits. Other processes such as resource limitation and selfing also need to be considered for a better understanding of floral trait evolution. |
format | Online Article Text |
id | pubmed-7517814 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-75178142020-09-29 Floral signals evolve in a predictable way under artificial and pollinator selection in Brassica rapa Zu, Pengjuan Schiestl, Florian P. Gervasi, Daniel Li, Xin Runcie, Daniel Guillaume, Frédéric BMC Evol Biol Research Article BACKGROUND: Angiosperms employ an astonishing variety of visual and olfactory floral signals that are generally thought to evolve under natural selection. Those morphological and chemical traits can form highly correlated sets of traits. It is not always clear which of these are used by pollinators as primary targets of selection and which would be indirectly selected by being linked to those primary targets. Quantitative genetics tools for predicting multiple traits response to selection have been developed since long and have advanced our understanding of evolution of genetically correlated traits in various biological systems. We use these tools to predict the evolutionary trajectories of floral traits and understand the selection pressures acting on them. RESULTS: We used data from an artificial selection and a pollinator (bumblebee, hoverfly) evolution experiment with fast cycling Brassica rapa plants to predict evolutionary changes of 12 floral volatiles and 4 morphological floral traits in response to selection. Using the observed selection gradients and the genetic variance-covariance matrix (G-matrix) of the traits, we showed that the observed responses of most floral traits including volatiles were predicted in the right direction in both artificial- and bumblebee-selection experiment. Genetic covariance had a mix of constraining and facilitating effects on evolutionary responses. We further revealed that G-matrices also evolved in the selection processes. CONCLUSIONS: Overall, our integrative study shows that floral signals, especially volatiles, evolve under selection in a mostly predictable way, at least during short term evolution. Evolutionary constraints stemming from genetic covariance affected traits evolutionary trajectories and thus it is important to include genetic covariance for predicting the evolutionary changes of a comprehensive suite of traits. Other processes such as resource limitation and selfing also need to be considered for a better understanding of floral trait evolution. BioMed Central 2020-09-24 /pmc/articles/PMC7517814/ /pubmed/32972368 http://dx.doi.org/10.1186/s12862-020-01692-7 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Zu, Pengjuan Schiestl, Florian P. Gervasi, Daniel Li, Xin Runcie, Daniel Guillaume, Frédéric Floral signals evolve in a predictable way under artificial and pollinator selection in Brassica rapa |
title | Floral signals evolve in a predictable way under artificial and pollinator selection in Brassica rapa |
title_full | Floral signals evolve in a predictable way under artificial and pollinator selection in Brassica rapa |
title_fullStr | Floral signals evolve in a predictable way under artificial and pollinator selection in Brassica rapa |
title_full_unstemmed | Floral signals evolve in a predictable way under artificial and pollinator selection in Brassica rapa |
title_short | Floral signals evolve in a predictable way under artificial and pollinator selection in Brassica rapa |
title_sort | floral signals evolve in a predictable way under artificial and pollinator selection in brassica rapa |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517814/ https://www.ncbi.nlm.nih.gov/pubmed/32972368 http://dx.doi.org/10.1186/s12862-020-01692-7 |
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